Overview As an hands-on AI/LLM Architect, you design and implement enterprise AI systems spanning classical ML, generative AI, and agentic architectures. You translate requirements into concrete designs, build components, and produce ADRs and diagrams to guide engineers. You work with cross-functional teams to Ground AI outputs in enterprise data, while balancing performance, cost, and reliability.

Design and deliver end-to-end software components for AI architectures Build AI agent architectures, prompts, tools, and memory systems Develop agent orchestration patterns with robust state management and error handling Integrate foundation models via fine-tuning, RAG, and custom integration Develop AI context layer with ingestion, context graphs, and knowledge grounding Implement embedding, vector storage, and retrieval in RAG pipelines Optimize model usage, latency, throughput, and cost Ensure systems meet security, governance, and Responsible AI standards Build observability, logging, tracing, monitoring,



and cost tracking Produce architecture artifacts (ADRs, blueprints, diagrams) for teams Collaborate with data engineers, ML engineers, and application developers Continuously learn new patterns and tools to stay enterprise-grade Proven experience designing and deploying enterprise-grade AI solutions with agentic, generative, and classical AI/ML on at least one cloud vendor Hands-on experience in agentic, LLM, and Generative AI space Strong Python programming skills Solid foundation in architecting and operationalizing LLM-driven architectures Professional experience coding, ML, DL, and NLP applications Several years as a machine learning architect delivering large-scale data and analytical systems Collaboration with cross-functional teams Python LLM architectures Agentic and generative AI

📌 AI Large Language Model (LLM) Technology Architect (Milano)
🏢 Accenture
📍 Milano